Introduction:

1. Introduction:

This project is based around the “Moneyball” theory of using statistical analysis to provide a greater insight into sport performances, in this case the selection/purchase of players from the 2018-19 season of the NBA who would help produce greater results for the Chicago Bulls organisation to improve on their past season result (finishing 13th in the Eastern Conference, and 27th overall on Win-Loss ratio) and provide an improved result for the upcoming 2019-2020 NBA season.

The assigned task included the following: * The assessment of potential players to purchase or retain for the Chicago Bulls organisation for the 2019-20 NBA season. * Projection of expected results with selected players. * Selection of 5 players, one from each position (Centre, Power Forward, Small Forward, Shooting Guard, Point Guard). * Ensure purchase of the 5 players was within the allotted budget of $118 million dollars. * The proposed purchases must allow enough budget to still field the other remaining players required for an NBA team (NBA teams are allowed 15 players total).

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The use of statistics in sport is not a new phenomeno `(Noauthor_undated-pl?)’ , partly due to people like Bill James and John Hollinger who implemented and revolutionised the use of statistical analysis, and is now common within sports like basketball and in particular the North American basketball league the NBA. John Hollinger created the all in one metric the Player Efficiency Rating or PER, which allowed for the collection of several variables (i.e both positive and negative outcomes e.g turnovers/)

The hypothesis for this project is based on the use of a combination of known analysis methods to create a predictive equation to aid in the selection of appropriate players for the Chicago Bulls 19/20 season in the NBA.

The variables were used to show an association of an increase in overall Win% due to an increase of points per minute played. The variables used for the predictive value were:

Effective Field Goal Percentage (eFGp) Trade Value (TrV) Efficiency rate (EFF) Usage Rate (Tm_use) Total Rebounds per minute (TRB_MP) Points per minute (PTS_per_MP)

This section should provide relevant background information and justification for the project, including:

1. relevant background information of basketball, including key metrics, position requirements etc

Key metrics: * Minutes played * Offensive value * Deffensive value (Rebounds) * Offensive value vs Defensive value * Assists * Points * Points per minute played * Rebounds * Offensive rebound % * Deffensive rebound % * Turnovers * Free Throw attempts * Free throw percentage * Attempts in the paint * Fouls * Regular season vs post season

** PER = Player efficiency rating *

From Wikipedia, the free encyclopedia Jump to navigationJump to search In basketball, effective field goal percentage (abbreviated eFG%) is a statistic that adjusts field goal percentage to account for the fact that three-point field goals count for three points while field goals only count for two points. [1] Its goal is to show what field goal percentage a two-point shooter would have to shoot at to match the output of a player who also shoots three-pointers. [2]

It is calculated by: \[ eFG(\%) =\frac{FG+(0.5*3P)}{FGA} \]

eDG% calculation

Total Rebounds/Minute

where:

FG = field goals made 3P = 3-point field goals made, FGA = field goal attempts, [3]

A rough approximation can also be had by:

\[ eFG(\%) =\frac{\frac{PPG-FT}{2}}{FGA} \]

where:

PPG = points per game FT = the free throws made FGA = field goal attempts The advantage of this second formula is that it highlights the aforementioned logic behind the statistic, where it is pretended that a player only shot two-point shots (hence the division of non-free-throw points by 2).

An additional formula that seems to be more in use by the statistics actually displayed on websites (but less cited by said websites) is: $$ eFG(%) =

$$

where:

2FG = 2-point field goals made 3FG = 3-point field goals made FGA = field goal attempts

2. Report scenario:

This report forms the tangible component of a reproducible data analysis project of a task given to the data analytics team by the Chicago Bulls GM . The task detailed the assessment of potential players to join/retain for the Chicago Bulls organisation for the 2019-20 NBA season.

The projected budget for player contracts for the 2019-20 season is $118 million dollars.

The aim of the project

This report and analysis aims to provide five starting players (PG, C, SG, PF & SF) of the highest value based upon a cost-benefit analysis. The purchase of the proposed athletes/players still allows sufficient budget to complete the remaining roster.

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This section should document your exploratory data analysis and may include but is not limited to:

1. checking for errors and missing values within the data sets

2. checking the distribution of variables

4. Data modelling and results:

[1] 0.483507

5. Player Analysis:

6. Player recommendations:

This section will be the key part that is presented to the general manager. Here you should present your recommendations for the best five starting players, but also think about what other important information they would want to know, and how it is best to present that information to them.

7. Summary:

Provide a brief summary which describes the key points and findings from your project. It will also be important to acknowledge any limitations of your model and overall approach to answering the question asked of you by the general manager.

7. Reference List

Provide a reference list of any sources you used in the development of your report and justification of your arguments. Please use the Vancouver reference style (Links to an external site.) for the reference list and in-text references.

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text To put multiple plots in a single row I set the out.width to 50% for two plots, 33% for 3 plots, or 25% to 4 plots, and set fig.align = “default.” Depending on what I’m trying to illustrate (e.g. show data or show plot variations), I’ll also tweak fig.width, as discussed below.

If you find that you’re having to squint to read the text in your plot, you need to tweak fig.width. If fig.width is larger than the size the figure is rendered in the final doc, the text will be too small; if fig.width is smaller, the text will be too big

fig.show = “hold” fig.cap = "" this will give caption and change figure to floating rather than inline

may need to re-add data frames between steps. If so, using cache = TRUe, dependson = “….”) allows that {r figure, include=FALSE} fig.width = 6 (6“) and fig.asp = 0.618 out.width =”70%" and fig.align = “center”

##```{r Table1 test, echo=FALSE} knitr::kable((model_testing), select(player_name, Tm, Age, Pos, salary, TrV, EFF, Tm_use_total,PTS_per_MP, TRB_MP, exp_PTS_per_MP) %>% arrange(desc(exp_PTS_per_MP), salary) %>% top_n(20), caption = “Top 20 player selections.”) # initial table